Parlor – Customer Collaboration Platform

Hacker News

Howdy HN! First off, I have to say I’ve been looking forward to this day for a long time! Over 3000 days ago I joined HN with the goal of eventually posting a “Show HN” for a startup I co-founded. Today, I get to finally do that with Parlor! I owe a ton to this community and truly believe I’ve learned something from HN just about every day since. Any feedback you have on Parlor would be greatly appreciated - even what you hate about it! (I mean you can’t really build a platform like ours if you’re averse to feedback.) Parlor is what we call a customer collaboration platform, and it’s designed to transform the way software teams engage their users in order to refine their products, validate their development efforts, and build longer lasting relationships with their users. Through Parlor, these software teams can validate the impact of all of the potential new feature ideas they’re considering building, by previewing them directly inside of their products, all without first having to write a single line of code. Parlor: https://teamparlor.com/ DEMOS for GOOD - let us demo Parlor to you and we’ll donate $25 to the charity of your choice. We’ll donate $25 to another charity of your choice if you give us 1 piece of actionable feedback or advice. (Why start a company if you can’t experiment with fun ways to drive growth?) Thanks! Jason

Visit product View on Hacker NewsDec 3, 2018

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Actual performance

6points

1comments

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, single · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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